Students Grade Prediction Model

This repository contains a machine learning model that predicts student grades based on the number of hours they studied. The model uses a simple linear regression technique to establish a relationship between the number of hours studied and the resulting grades.I have used scikit-learn to train this model.

Table of Contents

• Installation
• Model Explanation
• Visualization
• Predicting New Data

Installation

Prerequisites

• Python 3.6 or above
• Pip (Python package manager)

Required Python Packages

Make sure you have the following Python packages installed:

• numpy
• pandas
• matplotlib
• scikit-learn

You can install the required packages using the following command:

pip install numpy pandas matplotlib scikit-learn

Downloading the Model

Clone the repository:

git clone  https://github.com/nik1740/Grades_prediction

Load and use the pre-trained model:

import pickle
import numpy as np

# Load the trained model
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)

# Predict new data
new_hours = np.array([[9]])  # Example: Predicting for 9 hours studied
predicted_grade = model.predict(new_hours)
print(f"Predicted Grade for 9 hours of study: {predicted_grade[0]}")

Model Explanation

The model is built using a linear regression algorithm provided by the scikit-learn library. It takes the number of hours studied as the input (independent variable) and predicts the corresponding grade (dependent variable).

Steps in the Script:

Load Data: The script reads the CSV file containing the dataset.

Prepare Features and Target: The dataset is split into features (Hours_Studied) and target (Grades).

Split the Data: The data is split into training and testing sets.

Train the Model: A linear regression model is trained on the training data.

Make Predictions: The trained model makes predictions on the test data.

Evaluate the Model: The model's performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²).

Visualization

The script generates a scatter plot showing the correlation between the hours studied and the grades. The regression line is also plotted, showing the linear relationship established by the model.

To visualize the data:

plt.figure(figsize=(8, 6))
plt.scatter(X, y, color='blue', label='Actual Data')
plt.plot(X, model.predict(X), color='red', label='Regression Line')
plt.title('Hours Studied vs. Grades')
plt.xlabel('Hours Studied')
plt.ylabel('Grades')
plt.legend()
plt.show()

Predicting New Data

You can easily predict grades for new data points by modifying the new_hours variable in the script:

new_hours = np.array([[9]])  # Example: Predicting for 9 hours studied
predicted_score = model.predict(new_hours)
print(f"Predicted Grade for 9 hours of study: {predicted_score[0]}")